Overview

Dataset statistics

Number of variables47
Number of observations1758
Missing cells10
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory723.8 KiB
Average record size in memory421.6 B

Variable types

Numeric14
Categorical33

Alerts

Détecteur d'incendie(relié) has constant value ""Constant
Détecteur d'incendie(non relié) has constant value ""Constant
rooms is highly overall correlated with bedrooms and 1 other fieldsHigh correlation
bedrooms is highly overall correlated with roomsHigh correlation
washrooms is highly overall correlated with roomsHigh correlation
yard_area is highly overall correlated with total_parkingHigh correlation
year_certificate is highly overall correlated with has_certificateHigh correlation
total_parking is highly overall correlated with yard_areaHigh correlation
last_year_reno is highly overall correlated with has_renoHigh correlation
has_certificate is highly overall correlated with year_certificateHigh correlation
has_fireplace is highly overall correlated with fireplace_funcHigh correlation
fireplace_func is highly overall correlated with has_fireplaceHigh correlation
has_reno is highly overall correlated with last_year_renoHigh correlation
near_water is highly imbalanced (77.7%)Imbalance
has_pool is highly imbalanced (75.1%)Imbalance
Convecteurs is highly imbalanced (55.6%)Imbalance
Air soufflé (pulsé) is highly imbalanced (62.0%)Imbalance
Radiant is highly imbalanced (88.5%)Imbalance
Gaz naturel is highly imbalanced (99.3%)Imbalance
Poêle à bois is highly imbalanced (99.3%)Imbalance
Foyer au gaz is highly imbalanced (99.3%)Imbalance
water_access is highly imbalanced (96.7%)Imbalance
has_fireplace is highly imbalanced (58.7%)Imbalance
fireplace_func is highly imbalanced (60.7%)Imbalance
Porte de garage électrique is highly imbalanced (92.1%)Imbalance
Buanderie is highly imbalanced (92.1%)Imbalance
Aspirateur centrale is highly imbalanced (69.0%)Imbalance
Spa is highly imbalanced (98.7%)Imbalance
Adapté pour personne à mobilité réduite is highly imbalanced (98.2%)Imbalance
Interphone is highly imbalanced (95.8%)Imbalance
Fournaise is highly imbalanced (98.2%)Imbalance
Planchers chauffant is highly imbalanced (99.3%)Imbalance
Ascenseur is highly imbalanced (98.7%)Imbalance
Échangeur d'air is highly imbalanced (88.2%)Imbalance
Système d'alarme is highly imbalanced (71.3%)Imbalance
Borne de recharge is highly imbalanced (96.3%)Imbalance
ID is uniformly distributedUniform
ID has unique valuesUnique
year_certificate has 293 (16.7%) zerosZeros
last_year_reno has 1476 (84.0%) zerosZeros

Reproduction

Analysis started2023-10-06 00:07:48.274761
Analysis finished2023-10-06 00:07:58.146028
Duration9.87 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

ID
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct1758
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean902.65131
Minimum1
Maximum1807
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size92.0 KiB
2023-10-05T20:07:58.179756image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile91.7
Q1452.25
median899.5
Q31350.75
95-th percentile1714.15
Maximum1807
Range1806
Interquartile range (IQR)898.5

Descriptive statistics

Standard deviation520.53999
Coefficient of variation (CV)0.57667892
Kurtosis-1.1968067
Mean902.65131
Median Absolute Deviation (MAD)449.5
Skewness0.0012404057
Sum1586861
Variance270961.88
MonotonicityStrictly increasing
2023-10-05T20:07:58.222144image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.1%
1201 1
 
0.1%
1212 1
 
0.1%
1211 1
 
0.1%
1210 1
 
0.1%
1209 1
 
0.1%
1208 1
 
0.1%
1207 1
 
0.1%
1206 1
 
0.1%
1205 1
 
0.1%
Other values (1748) 1748
99.4%
ValueCountFrequency (%)
1 1
0.1%
2 1
0.1%
3 1
0.1%
4 1
0.1%
5 1
0.1%
6 1
0.1%
7 1
0.1%
8 1
0.1%
9 1
0.1%
10 1
0.1%
ValueCountFrequency (%)
1807 1
0.1%
1806 1
0.1%
1805 1
0.1%
1804 1
0.1%
1803 1
0.1%
1802 1
0.1%
1801 1
0.1%
1800 1
0.1%
1799 1
0.1%
1798 1
0.1%

price
Real number (ℝ)

Distinct345
Distinct (%)19.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean830171.78
Minimum700000
Maximum1000000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size92.0 KiB
2023-10-05T20:07:58.361456image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum700000
5-th percentile710000
Q1759000
median821000
Q3892500
95-th percentile980000
Maximum1000000
Range300000
Interquartile range (IQR)133500

Descriptive statistics

Standard deviation84244.046
Coefficient of variation (CV)0.10147785
Kurtosis-0.9318854
Mean830171.78
Median Absolute Deviation (MAD)67500
Skewness0.31942314
Sum1.459442 × 109
Variance7.0970593 × 109
MonotonicityNot monotonic
2023-10-05T20:07:58.402730image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
800000 52
 
3.0%
850000 50
 
2.8%
750000 50
 
2.8%
700000 47
 
2.7%
900000 45
 
2.6%
775000 40
 
2.3%
730000 37
 
2.1%
1000000 36
 
2.0%
950000 30
 
1.7%
725000 30
 
1.7%
Other values (335) 1341
76.3%
ValueCountFrequency (%)
700000 47
2.7%
700500 1
 
0.1%
701000 2
 
0.1%
701500 1
 
0.1%
702000 2
 
0.1%
702500 1
 
0.1%
703000 1
 
0.1%
703500 1
 
0.1%
705000 14
 
0.8%
706000 1
 
0.1%
ValueCountFrequency (%)
1000000 36
2.0%
999800 1
 
0.1%
999500 1
 
0.1%
999000 2
 
0.1%
998500 1
 
0.1%
997500 1
 
0.1%
997000 2
 
0.1%
996000 1
 
0.1%
995000 13
 
0.7%
992500 1
 
0.1%

units
Categorical

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size92.0 KiB
2
810 
3
676 
4
219 
5
 
46
6
 
7

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1758
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row3
5th row2

Common Values

ValueCountFrequency (%)
2 810
46.1%
3 676
38.5%
4 219
 
12.5%
5 46
 
2.6%
6 7
 
0.4%

Length

2023-10-05T20:07:58.438797image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-05T20:07:58.481367image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
2 810
46.1%
3 676
38.5%
4 219
 
12.5%
5 46
 
2.6%
6 7
 
0.4%

Most occurring characters

ValueCountFrequency (%)
2 810
46.1%
3 676
38.5%
4 219
 
12.5%
5 46
 
2.6%
6 7
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1758
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 810
46.1%
3 676
38.5%
4 219
 
12.5%
5 46
 
2.6%
6 7
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Common 1758
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 810
46.1%
3 676
38.5%
4 219
 
12.5%
5 46
 
2.6%
6 7
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1758
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 810
46.1%
3 676
38.5%
4 219
 
12.5%
5 46
 
2.6%
6 7
 
0.4%

income
Real number (ℝ)

Distinct668
Distinct (%)38.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40415.681
Minimum2200
Maximum141000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size92.0 KiB
2023-10-05T20:07:58.518372image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum2200
5-th percentile24000
Q134680
median40200
Q346800
95-th percentile56298
Maximum141000
Range138800
Interquartile range (IQR)12120

Descriptive statistics

Standard deviation10287.667
Coefficient of variation (CV)0.25454643
Kurtosis5.8164642
Mean40415.681
Median Absolute Deviation (MAD)6000
Skewness0.26975363
Sum71050767
Variance1.058361 × 108
MonotonicityNot monotonic
2023-10-05T20:07:58.556729image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
38400 38
 
2.2%
45600 32
 
1.8%
39600 31
 
1.8%
48000 29
 
1.6%
33600 26
 
1.5%
42000 26
 
1.5%
50400 24
 
1.4%
44400 23
 
1.3%
36000 22
 
1.3%
40800 21
 
1.2%
Other values (658) 1486
84.5%
ValueCountFrequency (%)
2200 1
 
0.1%
3660 1
 
0.1%
5520 1
 
0.1%
7800 1
 
0.1%
9000 3
0.2%
9240 1
 
0.1%
9480 1
 
0.1%
9780 2
0.1%
10080 1
 
0.1%
10140 1
 
0.1%
ValueCountFrequency (%)
141000 1
 
0.1%
78096 1
 
0.1%
78000 1
 
0.1%
71640 1
 
0.1%
67920 1
 
0.1%
67200 1
 
0.1%
67140 1
 
0.1%
66000 4
0.2%
65400 1
 
0.1%
65088 1
 
0.1%

land_eval
Real number (ℝ)

Distinct1197
Distinct (%)68.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean239195.97
Minimum40200
Maximum688600
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size92.0 KiB
2023-10-05T20:07:58.598037image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum40200
5-th percentile125385
Q1174225
median224450
Q3290825
95-th percentile403095
Maximum688600
Range648400
Interquartile range (IQR)116600

Descriptive statistics

Standard deviation88261.168
Coefficient of variation (CV)0.36899104
Kurtosis1.2394027
Mean239195.97
Median Absolute Deviation (MAD)56850
Skewness0.92918903
Sum4.2050651 × 108
Variance7.7900337 × 109
MonotonicityNot monotonic
2023-10-05T20:07:58.635533image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
181200 16
 
0.9%
271800 13
 
0.7%
199000 9
 
0.5%
145700 8
 
0.5%
195100 8
 
0.5%
390200 8
 
0.5%
243900 7
 
0.4%
125400 7
 
0.4%
255500 6
 
0.3%
365900 6
 
0.3%
Other values (1187) 1670
95.0%
ValueCountFrequency (%)
40200 1
 
0.1%
63900 1
 
0.1%
75200 1
 
0.1%
80000 1
 
0.1%
86100 1
 
0.1%
86200 1
 
0.1%
87400 3
0.2%
90000 1
 
0.1%
90300 1
 
0.1%
90500 1
 
0.1%
ValueCountFrequency (%)
688600 1
0.1%
670700 1
0.1%
641000 1
0.1%
624700 1
0.1%
564400 1
0.1%
561900 1
0.1%
549200 1
0.1%
543500 1
0.1%
535600 1
0.1%
526800 2
0.1%

build_eval
Real number (ℝ)

Distinct1424
Distinct (%)81.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean422901.23
Minimum124200
Maximum1188600
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size92.0 KiB
2023-10-05T20:07:58.679414image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum124200
5-th percentile256710
Q1337900
median406251.5
Q3493575
95-th percentile649210
Maximum1188600
Range1064400
Interquartile range (IQR)155675

Descriptive statistics

Standard deviation126012.31
Coefficient of variation (CV)0.29797101
Kurtosis2.6220154
Mean422901.23
Median Absolute Deviation (MAD)77050
Skewness1.0545023
Sum7.4346036 × 108
Variance1.5879102 × 1010
MonotonicityNot monotonic
2023-10-05T20:07:58.722625image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
440200 5
 
0.3%
386400 5
 
0.3%
397500 5
 
0.3%
425000 4
 
0.2%
441800 4
 
0.2%
497300 4
 
0.2%
359500 4
 
0.2%
458500 4
 
0.2%
381200 4
 
0.2%
364700 4
 
0.2%
Other values (1414) 1715
97.6%
ValueCountFrequency (%)
124200 1
0.1%
127300 1
0.1%
128600 1
0.1%
151300 1
0.1%
153400 1
0.1%
156200 1
0.1%
158800 1
0.1%
161600 1
0.1%
168300 1
0.1%
168800 1
0.1%
ValueCountFrequency (%)
1188600 1
0.1%
1103000 1
0.1%
1045400 1
0.1%
994700 1
0.1%
993800 1
0.1%
984700 1
0.1%
939400 1
0.1%
929600 1
0.1%
919800 1
0.1%
893500 1
0.1%

rooms
Real number (ℝ)

Distinct15
Distinct (%)0.9%
Missing5
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean6.5231033
Minimum1
Maximum16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size92.0 KiB
2023-10-05T20:07:58.756280image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q15
median6
Q38
95-th percentile11
Maximum16
Range15
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.2347038
Coefficient of variation (CV)0.34258292
Kurtosis0.95712271
Mean6.5231033
Median Absolute Deviation (MAD)1
Skewness0.93598876
Sum11435
Variance4.9939009
MonotonicityNot monotonic
2023-10-05T20:07:58.783542image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
5 432
24.6%
6 306
17.4%
7 238
13.5%
8 210
11.9%
4 197
11.2%
9 121
 
6.9%
10 79
 
4.5%
3 67
 
3.8%
11 42
 
2.4%
12 28
 
1.6%
Other values (5) 33
 
1.9%
ValueCountFrequency (%)
1 2
 
0.1%
3 67
 
3.8%
4 197
11.2%
5 432
24.6%
6 306
17.4%
7 238
13.5%
8 210
11.9%
9 121
 
6.9%
10 79
 
4.5%
11 42
 
2.4%
ValueCountFrequency (%)
16 1
 
0.1%
15 6
 
0.3%
14 7
 
0.4%
13 17
 
1.0%
12 28
 
1.6%
11 42
 
2.4%
10 79
 
4.5%
9 121
6.9%
8 210
11.9%
7 238
13.5%

bedrooms
Real number (ℝ)

Distinct8
Distinct (%)0.5%
Missing5
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean2.8049059
Minimum0
Maximum7
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size92.0 KiB
2023-10-05T20:07:58.813023image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q33
95-th percentile4
Maximum7
Range7
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.81833924
Coefficient of variation (CV)0.29175284
Kurtosis0.91186326
Mean2.8049059
Median Absolute Deviation (MAD)0
Skewness0.028899542
Sum4917
Variance0.66967911
MonotonicityNot monotonic
2023-10-05T20:07:58.839410image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
3 927
52.7%
2 449
25.5%
4 253
 
14.4%
1 99
 
5.6%
5 18
 
1.0%
6 5
 
0.3%
0 1
 
0.1%
7 1
 
0.1%
(Missing) 5
 
0.3%
ValueCountFrequency (%)
0 1
 
0.1%
1 99
 
5.6%
2 449
25.5%
3 927
52.7%
4 253
 
14.4%
5 18
 
1.0%
6 5
 
0.3%
7 1
 
0.1%
ValueCountFrequency (%)
7 1
 
0.1%
6 5
 
0.3%
5 18
 
1.0%
4 253
 
14.4%
3 927
52.7%
2 449
25.5%
1 99
 
5.6%
0 1
 
0.1%

washrooms
Real number (ℝ)

Distinct6
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4459613
Minimum0
Maximum7
Zeros5
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size92.0 KiB
2023-10-05T20:07:58.867783image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q32
95-th percentile2
Maximum7
Range7
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.58746454
Coefficient of variation (CV)0.40627957
Kurtosis4.1198373
Mean1.4459613
Median Absolute Deviation (MAD)0
Skewness1.2052077
Sum2542
Variance0.34511458
MonotonicityNot monotonic
2023-10-05T20:07:58.893610image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 1034
58.8%
2 654
37.2%
3 63
 
3.6%
0 5
 
0.3%
7 1
 
0.1%
4 1
 
0.1%
ValueCountFrequency (%)
0 5
 
0.3%
1 1034
58.8%
2 654
37.2%
3 63
 
3.6%
4 1
 
0.1%
7 1
 
0.1%
ValueCountFrequency (%)
7 1
 
0.1%
4 1
 
0.1%
3 63
 
3.6%
2 654
37.2%
1 1034
58.8%
0 5
 
0.3%

year_built
Real number (ℝ)

Distinct108
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1948.9039
Minimum1870
Maximum2020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size92.0 KiB
2023-10-05T20:07:58.929687image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1870
5-th percentile1910
Q11931
median1955
Q31964
95-th percentile1976
Maximum2020
Range150
Interquartile range (IQR)33

Descriptive statistics

Standard deviation22.54649
Coefficient of variation (CV)0.011568805
Kurtosis0.33591832
Mean1948.9039
Median Absolute Deviation (MAD)11
Skewness-0.76864097
Sum3426173
Variance508.3442
MonotonicityNot monotonic
2023-10-05T20:07:58.968831image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1910 100
 
5.7%
1965 63
 
3.6%
1968 60
 
3.4%
1957 60
 
3.4%
1967 58
 
3.3%
1966 58
 
3.3%
1951 55
 
3.1%
1963 55
 
3.1%
1958 55
 
3.1%
1962 54
 
3.1%
Other values (98) 1140
64.8%
ValueCountFrequency (%)
1870 1
 
0.1%
1875 4
 
0.2%
1880 4
 
0.2%
1885 12
 
0.7%
1890 4
 
0.2%
1892 1
 
0.1%
1895 1
 
0.1%
1900 47
2.7%
1902 2
 
0.1%
1903 2
 
0.1%
ValueCountFrequency (%)
2020 1
0.1%
2019 1
0.1%
2017 1
0.1%
2010 1
0.1%
2009 1
0.1%
2008 1
0.1%
2004 1
0.1%
2001 1
0.1%
2000 2
0.1%
1999 1
0.1%

living_area
Real number (ℝ)

Distinct1139
Distinct (%)64.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1517.4905
Minimum10.89
Maximum13530
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size92.0 KiB
2023-10-05T20:07:59.010585image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum10.89
5-th percentile856.442
Q11071.9525
median1239.6435
Q31800
95-th percentile2886.294
Maximum13530
Range13519.11
Interquartile range (IQR)728.0475

Descriptive statistics

Standard deviation809.10303
Coefficient of variation (CV)0.53318492
Kurtosis54.705312
Mean1517.4905
Median Absolute Deviation (MAD)234.29
Skewness4.9358417
Sum2667748.2
Variance654647.72
MonotonicityNot monotonic
2023-10-05T20:07:59.052250image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1200 48
 
2.7%
1000 29
 
1.6%
1050 23
 
1.3%
1260 18
 
1.0%
1280 18
 
1.0%
1344 17
 
1.0%
1125 16
 
0.9%
1080 15
 
0.9%
1120 15
 
0.9%
1140 13
 
0.7%
Other values (1129) 1546
87.9%
ValueCountFrequency (%)
10.89 2
0.1%
25 1
0.1%
63 1
0.1%
80 1
0.1%
217.8 1
0.1%
264.2 1
0.1%
319.9 1
0.1%
400 1
0.1%
476 1
0.1%
613.8 1
0.1%
ValueCountFrequency (%)
13530 1
0.1%
11961.43 1
0.1%
11069.76 1
0.1%
5626 1
0.1%
5550 1
0.1%
4976.802 1
0.1%
4803 1
0.1%
4563.9 1
0.1%
4433.65 1
0.1%
4101.05 1
0.1%

yard_area
Real number (ℝ)

Distinct1274
Distinct (%)72.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3054.2685
Minimum95.69
Maximum26823.65
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size92.0 KiB
2023-10-05T20:07:59.094290image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum95.69
5-th percentile1699.527
Q12208.26
median2848.5
Q33676.45
95-th percentile5003.1
Maximum26823.65
Range26727.96
Interquartile range (IQR)1468.19

Descriptive statistics

Standard deviation1270.6153
Coefficient of variation (CV)0.41601298
Kurtosis73.461745
Mean3054.2685
Median Absolute Deviation (MAD)705.955
Skewness4.817478
Sum5369404.1
Variance1614463.4
MonotonicityNot monotonic
2023-10-05T20:07:59.135602image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2500.46 22
 
1.3%
2249.66 18
 
1.0%
2999.9 14
 
0.8%
1999.93 12
 
0.7%
2625 11
 
0.6%
1950 11
 
0.6%
2500 11
 
0.6%
1950.42 10
 
0.6%
3999.87 9
 
0.5%
2250 9
 
0.5%
Other values (1264) 1631
92.8%
ValueCountFrequency (%)
95.69 1
0.1%
162 1
0.1%
171 1
0.1%
176.5 1
0.1%
785 1
0.1%
786 1
0.1%
1008 1
0.1%
1041.95 1
0.1%
1176.49 1
0.1%
1179.72 1
0.1%
ValueCountFrequency (%)
26823.65 1
0.1%
14545.27 1
0.1%
8543.31 1
0.1%
8175.19 1
0.1%
8062.17 1
0.1%
7768.96 1
0.1%
7349.59 1
0.1%
7332.37 1
0.1%
7324 1
0.1%
7204 1
0.1%

has_certificate
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size92.0 KiB
1
1465 
0
293 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1758
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 1465
83.3%
0 293
 
16.7%

Length

2023-10-05T20:07:59.169553image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-05T20:07:59.203695image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
1 1465
83.3%
0 293
 
16.7%

Most occurring characters

ValueCountFrequency (%)
1 1465
83.3%
0 293
 
16.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1758
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1465
83.3%
0 293
 
16.7%

Most occurring scripts

ValueCountFrequency (%)
Common 1758
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1465
83.3%
0 293
 
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1758
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1465
83.3%
0 293
 
16.7%

year_certificate
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct72
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1675.8686
Minimum0
Maximum2023
Zeros293
Zeros (%)16.7%
Negative0
Negative (%)0.0%
Memory size92.0 KiB
2023-10-05T20:07:59.237308image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11991
median2013
Q32021
95-th percentile2023
Maximum2023
Range2023
Interquartile range (IQR)30

Descriptive statistics

Standard deviation749.80827
Coefficient of variation (CV)0.44741471
Kurtosis1.2044357
Mean1675.8686
Median Absolute Deviation (MAD)9
Skewness-1.7890647
Sum2946177
Variance562212.44
MonotonicityNot monotonic
2023-10-05T20:07:59.278458image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 293
16.7%
2022 270
15.4%
2021 152
 
8.6%
2023 125
 
7.1%
2020 56
 
3.2%
2017 53
 
3.0%
2019 48
 
2.7%
2016 42
 
2.4%
2018 42
 
2.4%
2015 41
 
2.3%
Other values (62) 636
36.2%
ValueCountFrequency (%)
0 293
16.7%
1947 1
 
0.1%
1948 1
 
0.1%
1949 1
 
0.1%
1950 1
 
0.1%
1951 2
 
0.1%
1952 2
 
0.1%
1955 1
 
0.1%
1957 4
 
0.2%
1958 2
 
0.1%
ValueCountFrequency (%)
2023 125
7.1%
2022 270
15.4%
2021 152
8.6%
2020 56
 
3.2%
2019 48
 
2.7%
2018 42
 
2.4%
2017 53
 
3.0%
2016 42
 
2.4%
2015 41
 
2.3%
2014 40
 
2.3%

due_certificate
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size92.0 KiB
0
1200 
1
558 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1758
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 1200
68.3%
1 558
31.7%

Length

2023-10-05T20:07:59.317079image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-05T20:07:59.349919image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1200
68.3%
1 558
31.7%

Most occurring characters

ValueCountFrequency (%)
0 1200
68.3%
1 558
31.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1758
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1200
68.3%
1 558
31.7%

Most occurring scripts

ValueCountFrequency (%)
Common 1758
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1200
68.3%
1 558
31.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1758
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1200
68.3%
1 558
31.7%

near_water
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size92.0 KiB
0
1695 
1
 
63

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1758
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1695
96.4%
1 63
 
3.6%

Length

2023-10-05T20:07:59.377316image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-05T20:07:59.411313image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1695
96.4%
1 63
 
3.6%

Most occurring characters

ValueCountFrequency (%)
0 1695
96.4%
1 63
 
3.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1758
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1695
96.4%
1 63
 
3.6%

Most occurring scripts

ValueCountFrequency (%)
Common 1758
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1695
96.4%
1 63
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1758
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1695
96.4%
1 63
 
3.6%

has_pool
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size92.0 KiB
0
1685 
1
 
73

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1758
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1685
95.8%
1 73
 
4.2%

Length

2023-10-05T20:07:59.438332image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-05T20:07:59.472215image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1685
95.8%
1 73
 
4.2%

Most occurring characters

ValueCountFrequency (%)
0 1685
95.8%
1 73
 
4.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1758
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1685
95.8%
1 73
 
4.2%

Most occurring scripts

ValueCountFrequency (%)
Common 1758
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1685
95.8%
1 73
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1758
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1685
95.8%
1 73
 
4.2%

total_parking
Real number (ℝ)

Distinct10
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1370876
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size92.0 KiB
2023-10-05T20:07:59.497238image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile4
Maximum12
Range11
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.2869384
Coefficient of variation (CV)0.60219264
Kurtosis4.7199943
Mean2.1370876
Median Absolute Deviation (MAD)1
Skewness1.6311766
Sum3757
Variance1.6562105
MonotonicityNot monotonic
2023-10-05T20:07:59.525551image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 684
38.9%
2 556
31.6%
3 244
 
13.9%
4 205
 
11.7%
5 36
 
2.0%
6 18
 
1.0%
7 7
 
0.4%
8 5
 
0.3%
10 2
 
0.1%
12 1
 
0.1%
ValueCountFrequency (%)
1 684
38.9%
2 556
31.6%
3 244
 
13.9%
4 205
 
11.7%
5 36
 
2.0%
6 18
 
1.0%
7 7
 
0.4%
8 5
 
0.3%
10 2
 
0.1%
12 1
 
0.1%
ValueCountFrequency (%)
12 1
 
0.1%
10 2
 
0.1%
8 5
 
0.3%
7 7
 
0.4%
6 18
 
1.0%
5 36
 
2.0%
4 205
 
11.7%
3 244
 
13.9%
2 556
31.6%
1 684
38.9%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size92.0 KiB
1
1485 
0
273 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1758
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 1485
84.5%
0 273
 
15.5%

Length

2023-10-05T20:07:59.555851image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-05T20:07:59.588713image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
1 1485
84.5%
0 273
 
15.5%

Most occurring characters

ValueCountFrequency (%)
1 1485
84.5%
0 273
 
15.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1758
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1485
84.5%
0 273
 
15.5%

Most occurring scripts

ValueCountFrequency (%)
Common 1758
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1485
84.5%
0 273
 
15.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1758
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1485
84.5%
0 273
 
15.5%

Convecteurs
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size92.0 KiB
0
1596 
1
162 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1758
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1596
90.8%
1 162
 
9.2%

Length

2023-10-05T20:07:59.617124image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-05T20:07:59.650646image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1596
90.8%
1 162
 
9.2%

Most occurring characters

ValueCountFrequency (%)
0 1596
90.8%
1 162
 
9.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1758
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1596
90.8%
1 162
 
9.2%

Most occurring scripts

ValueCountFrequency (%)
Common 1758
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1596
90.8%
1 162
 
9.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1758
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1596
90.8%
1 162
 
9.2%

Eau chaude
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size92.0 KiB
0
1335 
1
423 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1758
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1335
75.9%
1 423
 
24.1%

Length

2023-10-05T20:07:59.679076image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-05T20:07:59.713013image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1335
75.9%
1 423
 
24.1%

Most occurring characters

ValueCountFrequency (%)
0 1335
75.9%
1 423
 
24.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1758
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1335
75.9%
1 423
 
24.1%

Most occurring scripts

ValueCountFrequency (%)
Common 1758
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1335
75.9%
1 423
 
24.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1758
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1335
75.9%
1 423
 
24.1%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size92.0 KiB
0
1628 
1
 
130

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1758
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1628
92.6%
1 130
 
7.4%

Length

2023-10-05T20:07:59.740920image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-05T20:07:59.867414image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1628
92.6%
1 130
 
7.4%

Most occurring characters

ValueCountFrequency (%)
0 1628
92.6%
1 130
 
7.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1758
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1628
92.6%
1 130
 
7.4%

Most occurring scripts

ValueCountFrequency (%)
Common 1758
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1628
92.6%
1 130
 
7.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1758
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1628
92.6%
1 130
 
7.4%

Radiant
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size92.0 KiB
0
1731 
1
 
27

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1758
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1731
98.5%
1 27
 
1.5%

Length

2023-10-05T20:07:59.894494image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-05T20:07:59.928829image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1731
98.5%
1 27
 
1.5%

Most occurring characters

ValueCountFrequency (%)
0 1731
98.5%
1 27
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1758
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1731
98.5%
1 27
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
Common 1758
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1731
98.5%
1 27
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1758
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1731
98.5%
1 27
 
1.5%

Thermopompe
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size92.0 KiB
0
1481 
1
277 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1758
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 1481
84.2%
1 277
 
15.8%

Length

2023-10-05T20:07:59.956594image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-05T20:07:59.990391image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1481
84.2%
1 277
 
15.8%

Most occurring characters

ValueCountFrequency (%)
0 1481
84.2%
1 277
 
15.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1758
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1481
84.2%
1 277
 
15.8%

Most occurring scripts

ValueCountFrequency (%)
Common 1758
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1481
84.2%
1 277
 
15.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1758
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1481
84.2%
1 277
 
15.8%

Gaz naturel
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size92.0 KiB
0
1757 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1758
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1757
99.9%
1 1
 
0.1%

Length

2023-10-05T20:08:00.018478image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-05T20:08:00.052418image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1757
99.9%
1 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 1757
99.9%
1 1
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1758
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1757
99.9%
1 1
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 1758
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1757
99.9%
1 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1758
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1757
99.9%
1 1
 
0.1%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size92.0 KiB
0
1757 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1758
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1757
99.9%
1 1
 
0.1%

Length

2023-10-05T20:08:00.078887image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-05T20:08:00.111611image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1757
99.9%
1 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 1757
99.9%
1 1
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1758
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1757
99.9%
1 1
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 1758
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1757
99.9%
1 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1758
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1757
99.9%
1 1
 
0.1%

Foyer au gaz
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size92.0 KiB
0
1757 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1758
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1757
99.9%
1 1
 
0.1%

Length

2023-10-05T20:08:00.140588image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-05T20:08:00.173026image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1757
99.9%
1 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 1757
99.9%
1 1
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1758
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1757
99.9%
1 1
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 1758
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1757
99.9%
1 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1758
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1757
99.9%
1 1
 
0.1%

water_access
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size92.0 KiB
0
1752 
1
 
6

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1758
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1752
99.7%
1 6
 
0.3%

Length

2023-10-05T20:08:00.201020image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-05T20:08:00.233831image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1752
99.7%
1 6
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 1752
99.7%
1 6
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1758
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1752
99.7%
1 6
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common 1758
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1752
99.7%
1 6
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1758
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1752
99.7%
1 6
 
0.3%

has_fireplace
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size92.0 KiB
0
1612 
1
 
146

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1758
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1612
91.7%
1 146
 
8.3%

Length

2023-10-05T20:08:00.261539image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-05T20:08:00.294584image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1612
91.7%
1 146
 
8.3%

Most occurring characters

ValueCountFrequency (%)
0 1612
91.7%
1 146
 
8.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1758
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1612
91.7%
1 146
 
8.3%

Most occurring scripts

ValueCountFrequency (%)
Common 1758
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1612
91.7%
1 146
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1758
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1612
91.7%
1 146
 
8.3%

fireplace_func
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size92.0 KiB
0
1622 
1
 
136

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1758
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1622
92.3%
1 136
 
7.7%

Length

2023-10-05T20:08:00.324860image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-05T20:08:00.359045image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1622
92.3%
1 136
 
7.7%

Most occurring characters

ValueCountFrequency (%)
0 1622
92.3%
1 136
 
7.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1758
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1622
92.3%
1 136
 
7.7%

Most occurring scripts

ValueCountFrequency (%)
Common 1758
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1622
92.3%
1 136
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1758
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1622
92.3%
1 136
 
7.7%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size92.0 KiB
0
1741 
1
 
17

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1758
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1741
99.0%
1 17
 
1.0%

Length

2023-10-05T20:08:00.386224image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-05T20:08:00.420343image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1741
99.0%
1 17
 
1.0%

Most occurring characters

ValueCountFrequency (%)
0 1741
99.0%
1 17
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1758
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1741
99.0%
1 17
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1758
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1741
99.0%
1 17
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1758
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1741
99.0%
1 17
 
1.0%

Buanderie
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size92.0 KiB
0
1741 
1
 
17

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1758
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1741
99.0%
1 17
 
1.0%

Length

2023-10-05T20:08:00.447258image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-05T20:08:00.481740image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1741
99.0%
1 17
 
1.0%

Most occurring characters

ValueCountFrequency (%)
0 1741
99.0%
1 17
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1758
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1741
99.0%
1 17
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1758
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1741
99.0%
1 17
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1758
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1741
99.0%
1 17
 
1.0%

Climatiseur
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size92.0 KiB
0
1481 
1
277 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1758
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1481
84.2%
1 277
 
15.8%

Length

2023-10-05T20:08:00.508481image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-05T20:08:00.541586image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1481
84.2%
1 277
 
15.8%

Most occurring characters

ValueCountFrequency (%)
0 1481
84.2%
1 277
 
15.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1758
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1481
84.2%
1 277
 
15.8%

Most occurring scripts

ValueCountFrequency (%)
Common 1758
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1481
84.2%
1 277
 
15.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1758
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1481
84.2%
1 277
 
15.8%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size92.0 KiB
0
1660 
1
 
98

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1758
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1660
94.4%
1 98
 
5.6%

Length

2023-10-05T20:08:00.569903image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-05T20:08:00.602424image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1660
94.4%
1 98
 
5.6%

Most occurring characters

ValueCountFrequency (%)
0 1660
94.4%
1 98
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1758
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1660
94.4%
1 98
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
Common 1758
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1660
94.4%
1 98
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1758
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1660
94.4%
1 98
 
5.6%

Spa
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size92.0 KiB
0
1756 
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1758
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1756
99.9%
1 2
 
0.1%

Length

2023-10-05T20:08:00.630233image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-05T20:08:00.663675image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1756
99.9%
1 2
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 1756
99.9%
1 2
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1758
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1756
99.9%
1 2
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 1758
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1756
99.9%
1 2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1758
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1756
99.9%
1 2
 
0.1%
Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size92.0 KiB
0
1758 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1758
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1758
100.0%

Length

2023-10-05T20:08:00.691286image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-05T20:08:00.723333image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1758
100.0%

Most occurring characters

ValueCountFrequency (%)
0 1758
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1758
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1758
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1758
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1758
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1758
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1758
100.0%
Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size92.0 KiB
0
1758 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1758
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1758
100.0%

Length

2023-10-05T20:08:00.749207image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-05T20:08:00.781950image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1758
100.0%

Most occurring characters

ValueCountFrequency (%)
0 1758
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1758
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1758
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1758
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1758
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1758
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1758
100.0%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size92.0 KiB
0
1755 
1
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1758
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1755
99.8%
1 3
 
0.2%

Length

2023-10-05T20:08:00.808046image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-05T20:08:00.842157image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1755
99.8%
1 3
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 1755
99.8%
1 3
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1758
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1755
99.8%
1 3
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 1758
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1755
99.8%
1 3
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1758
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1755
99.8%
1 3
 
0.2%

Interphone
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size92.0 KiB
0
1750 
1
 
8

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1758
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1750
99.5%
1 8
 
0.5%

Length

2023-10-05T20:08:00.869088image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-05T20:08:00.901762image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1750
99.5%
1 8
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 1750
99.5%
1 8
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1758
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1750
99.5%
1 8
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Common 1758
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1750
99.5%
1 8
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1758
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1750
99.5%
1 8
 
0.5%

Fournaise
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size92.0 KiB
0
1755 
1
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1758
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1755
99.8%
1 3
 
0.2%

Length

2023-10-05T20:08:00.929628image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-05T20:08:00.962467image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1755
99.8%
1 3
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 1755
99.8%
1 3
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1758
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1755
99.8%
1 3
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 1758
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1755
99.8%
1 3
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1758
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1755
99.8%
1 3
 
0.2%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size92.0 KiB
0
1757 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1758
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1757
99.9%
1 1
 
0.1%

Length

2023-10-05T20:08:00.989761image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-05T20:08:01.022425image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1757
99.9%
1 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 1757
99.9%
1 1
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1758
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1757
99.9%
1 1
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 1758
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1757
99.9%
1 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1758
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1757
99.9%
1 1
 
0.1%

Ascenseur
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size92.0 KiB
0
1756 
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1758
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1756
99.9%
1 2
 
0.1%

Length

2023-10-05T20:08:01.049134image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-05T20:08:01.082638image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1756
99.9%
1 2
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 1756
99.9%
1 2
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1758
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1756
99.9%
1 2
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 1758
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1756
99.9%
1 2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1758
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1756
99.9%
1 2
 
0.1%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size92.0 KiB
0
1730 
1
 
28

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1758
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1730
98.4%
1 28
 
1.6%

Length

2023-10-05T20:08:01.109327image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-05T20:08:01.143245image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1730
98.4%
1 28
 
1.6%

Most occurring characters

ValueCountFrequency (%)
0 1730
98.4%
1 28
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1758
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1730
98.4%
1 28
 
1.6%

Most occurring scripts

ValueCountFrequency (%)
Common 1758
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1730
98.4%
1 28
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1758
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1730
98.4%
1 28
 
1.6%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size92.0 KiB
0
1670 
1
 
88

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1758
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1670
95.0%
1 88
 
5.0%

Length

2023-10-05T20:08:01.170704image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-05T20:08:01.204718image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1670
95.0%
1 88
 
5.0%

Most occurring characters

ValueCountFrequency (%)
0 1670
95.0%
1 88
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1758
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1670
95.0%
1 88
 
5.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1758
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1670
95.0%
1 88
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1758
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1670
95.0%
1 88
 
5.0%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size92.0 KiB
0
1751 
1
 
7

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1758
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1751
99.6%
1 7
 
0.4%

Length

2023-10-05T20:08:01.231515image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-05T20:08:01.264291image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1751
99.6%
1 7
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 1751
99.6%
1 7
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1758
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1751
99.6%
1 7
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Common 1758
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1751
99.6%
1 7
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1758
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1751
99.6%
1 7
 
0.4%

has_reno
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size92.0 KiB
0
1476 
1
282 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1758
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1476
84.0%
1 282
 
16.0%

Length

2023-10-05T20:08:01.292780image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-05T20:08:01.326805image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1476
84.0%
1 282
 
16.0%

Most occurring characters

ValueCountFrequency (%)
0 1476
84.0%
1 282
 
16.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1758
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1476
84.0%
1 282
 
16.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1758
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1476
84.0%
1 282
 
16.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1758
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1476
84.0%
1 282
 
16.0%

last_year_reno
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct23
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean330.2281
Minimum0
Maximum8937
Zeros1476
Zeros (%)84.0%
Negative0
Negative (%)0.0%
Memory size92.0 KiB
2023-10-05T20:08:01.356497image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2021
Maximum8937
Range8937
Interquartile range (IQR)0

Descriptive statistics

Standard deviation780.93008
Coefficient of variation (CV)2.3648202
Kurtosis11.257065
Mean330.2281
Median Absolute Deviation (MAD)0
Skewness2.5997759
Sum580541
Variance609851.79
MonotonicityNot monotonic
2023-10-05T20:08:01.386928image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0 1476
84.0%
2021 53
 
3.0%
2020 43
 
2.4%
2022 32
 
1.8%
2019 28
 
1.6%
2018 18
 
1.0%
2016 18
 
1.0%
2017 16
 
0.9%
2015 14
 
0.8%
2012 10
 
0.6%
Other values (13) 50
 
2.8%
ValueCountFrequency (%)
0 1476
84.0%
2000 1
 
0.1%
2003 1
 
0.1%
2005 1
 
0.1%
2006 4
 
0.2%
2008 3
 
0.2%
2009 4
 
0.2%
2010 4
 
0.2%
2011 3
 
0.2%
2012 10
 
0.6%
ValueCountFrequency (%)
8937 1
 
0.1%
6565 1
 
0.1%
2023 9
 
0.5%
2022 32
1.8%
2021 53
3.0%
2020 43
2.4%
2019 28
1.6%
2018 18
 
1.0%
2017 16
 
0.9%
2016 18
 
1.0%

Interactions

2023-10-05T20:07:57.253836image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:50.261603image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:50.783985image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:51.302710image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:51.826080image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:52.430512image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:52.939720image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:53.452828image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:53.965509image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:54.569965image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:55.096284image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:55.625102image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:56.128849image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:56.753633image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:57.289150image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:50.303844image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:50.820494image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:51.340103image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:51.863946image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:52.466909image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:52.975837image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:53.489263image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:54.002033image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:54.607051image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:55.134207image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:55.661356image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:56.167101image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:56.789324image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:57.328550image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:50.341971image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:50.857823image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:51.379276image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:51.901441image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:52.503171image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:53.013700image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:53.526541image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:54.038744image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:54.645243image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:55.171790image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:55.697915image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:56.205568image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:56.825685image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:57.365351image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:50.379919image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:50.895904image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:51.415930image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:52.024041image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:52.540557image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:53.049876image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:53.563426image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:54.169029image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:54.682832image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:55.211521image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:55.735145image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:56.339117image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:56.862570image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:57.400216image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:50.417326image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:50.933436image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:51.454383image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:52.059501image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:52.577068image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:53.087963image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:53.600819image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:54.206648image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:54.720995image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:55.249053image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:55.772224image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:56.377891image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:56.898023image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:57.435536image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:50.453893image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:50.969175image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:51.490671image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:52.095721image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:52.611124image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:53.123325image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:53.636380image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:54.241407image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:54.756983image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:55.286346image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:55.806900image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:56.414847image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:56.934400image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:57.470398image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:50.491389image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:51.006984image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:51.528457image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:52.133792image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:52.647502image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:53.159796image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:53.674173image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:54.278814image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:54.795647image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:55.324071image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:55.843792image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:56.451569image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:56.969952image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:57.506748image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:50.529008image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:51.043417image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:51.566408image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:52.170925image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:52.687274image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:53.196848image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:53.710150image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:54.315132image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:54.832691image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:55.362302image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:55.880769image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:56.490095image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:57.006660image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:57.543045image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:50.566562image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:51.081628image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:51.604646image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:52.208302image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:52.724113image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:53.233253image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:53.746231image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:54.351635image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:54.871307image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:55.399897image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:55.917786image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:56.529354image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:57.042953image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:57.579889image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:50.604731image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:51.119188image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:51.643100image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:52.246701image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:52.762485image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:53.270662image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:53.785134image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:54.389431image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:54.908493image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:55.440653image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:55.954552image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:56.567528image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:57.079262image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:57.615573image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:50.641685image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:51.157370image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:51.682524image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:52.285500image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:52.799362image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:53.310376image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:53.823086image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:54.427352image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:54.948223image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:55.478018image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:55.991566image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:56.606491image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:57.115672image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:57.649167image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:50.676184image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:51.192434image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:51.717726image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:52.320648image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:52.834230image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:53.344500image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:53.857574image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:54.461642image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:54.984040image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:55.514755image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:56.023998image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:56.641706image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:57.149624image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:57.687879image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:50.714484image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:51.232092image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:51.757288image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:52.361041image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:52.872790image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:53.383433image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:53.897012image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:54.501326image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:55.025865image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:55.553849image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:56.061641image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:56.680581image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:57.187954image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:57.720917image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:50.749089image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:51.267923image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:51.791730image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:52.396736image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:52.906339image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:53.417900image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:53.931744image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:54.535844image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:55.061881image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:55.590657image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:56.096146image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:56.717499image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-10-05T20:07:57.220319image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2023-10-05T20:08:01.437547image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
IDpriceincomeland_evalbuild_evalroomsbedroomswashroomsyear_builtliving_areayard_areayear_certificatetotal_parkinglast_year_renounitshas_certificatedue_certificatenear_waterhas_poolPlinthes électriquesConvecteursEau chaudeAir soufflé (pulsé)RadiantThermopompeGaz naturelPoêle à boisFoyer au gazwater_accesshas_fireplacefireplace_funcPorte de garage électriqueBuanderieClimatiseurAspirateur centraleSpaAdapté pour personne à mobilité réduiteInterphoneFournaisePlanchers chauffantAscenseurÉchangeur d'airSystème d'alarmeBorne de rechargehas_reno
ID1.0000.0130.0010.017-0.0240.0310.026-0.0280.008-0.0160.014-0.0130.062-0.0140.0910.0000.0040.0900.0560.0630.0000.1100.0620.0530.0000.0000.0000.0000.0000.1560.1650.0710.0000.0800.0370.0000.0000.0000.0000.0080.0000.0090.0750.0890.033
price0.0131.0000.4670.2820.2550.0530.0330.084-0.0710.1770.123-0.0070.059-0.0040.1180.0550.0000.0290.0230.0050.0870.0550.0770.0000.0290.0000.0000.0000.0390.0880.0630.0390.0080.0860.0370.0110.0000.0000.0350.0000.0990.0000.0510.0000.000
income0.0010.4671.0000.1850.3510.0170.0180.0300.0030.1740.0970.0980.0640.0330.1900.0000.0510.0480.0000.0200.0130.0000.0000.0260.0610.0000.0500.0000.0000.0000.0000.0000.0000.0000.0240.0000.0640.0000.0000.0000.0000.0000.0480.0000.000
land_eval0.0170.2820.1851.0000.2740.1500.0800.081-0.1530.1440.3930.1200.158-0.0240.0520.0280.0570.0000.0600.0850.0500.1240.0460.0000.0000.0000.0000.0000.1030.0000.0000.0000.0000.0570.0230.0000.0000.0000.0620.0000.0000.0340.0000.0000.000
build_eval-0.0240.2550.3510.2741.0000.0770.0680.0580.1020.181-0.0700.0890.0260.0320.1310.0000.0510.0960.0000.0000.0000.0000.0090.0000.0000.0230.1130.0000.0000.0720.0610.0000.0000.0880.0350.0760.0200.0000.0000.0000.0620.0690.0000.0000.000
rooms0.0310.0530.0170.1500.0771.0000.6050.5880.1800.0680.1850.0400.2010.0470.1560.0300.0000.0220.0900.1310.0700.1630.1110.0400.1360.0000.0000.0000.0000.1220.1110.0580.0700.1280.1310.0000.0000.1260.0000.0000.0000.0000.0910.0000.074
bedrooms0.0260.0330.0180.0800.0680.6051.0000.4440.3190.1580.289-0.0200.2590.0130.1150.0250.0000.0000.0560.1280.0000.1060.0770.0000.0000.0000.0000.0000.0000.0580.0620.0490.0000.0750.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
washrooms-0.0280.0840.0300.0810.0580.5880.4441.0000.2450.0730.1870.0060.1840.0070.4500.0210.0260.0000.0390.1370.0000.0980.0960.0400.1120.0000.0000.0000.0000.1470.1510.0650.0530.1560.0840.0000.0000.1510.0000.0000.0000.0000.0460.0000.116
year_built0.008-0.0710.003-0.1530.1020.1800.3190.2451.0000.1450.442-0.0190.4600.0210.1140.0000.0000.0000.0700.1620.0970.2020.1220.0530.1250.0000.0370.0000.0250.1930.1910.0750.0000.1580.0390.0000.0000.0330.0000.0000.2120.2990.0790.0000.000
living_area-0.0160.1770.1740.1440.1810.0680.1580.0730.1451.0000.295-0.0720.1480.0190.0970.1000.0710.0740.0000.0000.0000.0220.0420.0000.0510.0000.0000.0000.0000.0740.0760.0000.0000.0140.0000.0000.0000.0000.0000.0000.0320.0000.0000.0000.000
yard_area0.0140.1230.0970.393-0.0700.1850.2890.1870.4420.2951.0000.0540.532-0.0070.1380.0000.0300.1450.1910.1130.0000.1370.0770.0510.0840.0000.0000.0000.3010.1670.1650.0000.0000.0860.0370.0000.0000.2460.0000.0000.0000.0000.0000.0000.061
year_certificate-0.013-0.0070.0980.1200.0890.040-0.0200.006-0.019-0.0720.0541.0000.0790.0230.0000.9980.3020.0060.0000.0000.0170.0000.0000.0290.0330.0000.0000.0000.0000.0000.0000.0000.0000.0220.0000.0470.0280.0000.0000.0000.0000.0000.0170.0000.032
total_parking0.0620.0590.0640.1580.0260.2010.2590.1840.4600.1480.5320.0791.000-0.0080.0890.0340.0000.0360.1020.0870.0000.0720.0900.0000.0810.0000.0000.0000.0760.1500.1510.0000.0000.1280.0580.0000.0000.0700.0000.0000.0000.0000.0970.0000.053
last_year_reno-0.014-0.0040.033-0.0240.0320.0470.0130.0070.0210.019-0.0070.023-0.0081.0000.0550.0130.0000.0000.1190.0000.0000.0000.0750.0000.0670.0000.0000.0000.0000.0170.0000.0000.0000.0660.0490.0000.0000.0000.0000.0000.0000.0390.0670.0870.999
units0.0910.1180.1900.0520.1310.1560.1150.4500.1140.0970.1380.0000.0890.0551.0000.0000.0380.0100.0130.1070.0420.0800.0770.0000.1450.0000.0000.0000.0000.0400.0500.0000.0000.1170.0920.0000.0210.1230.0000.0000.0000.0000.0450.0000.094
has_certificate0.0000.0550.0000.0280.0000.0300.0250.0210.0000.1000.0000.9980.0340.0130.0001.0000.3020.0060.0000.0000.0170.0000.0000.0290.0330.0000.0000.0000.0000.0000.0000.0000.0000.0220.0000.0470.0280.0000.0000.0000.0000.0000.0170.0000.032
due_certificate0.0040.0000.0510.0570.0510.0000.0000.0260.0000.0710.0300.3020.0000.0000.0380.3021.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0170.0000.0000.0000.0110.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
near_water0.0900.0290.0480.0000.0960.0220.0000.0000.0000.0740.1450.0060.0360.0000.0100.0060.0001.0000.0000.0000.0390.0110.0230.0000.0000.0000.0000.0000.2760.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
has_pool0.0560.0230.0000.0600.0000.0900.0560.0390.0700.0000.1910.0000.1020.1190.0130.0000.0000.0001.0000.0000.0290.0000.0380.0000.0750.0490.0000.0000.0000.0620.0690.0000.0000.0660.1400.0260.0000.0000.0000.0000.0000.0190.0290.0490.047
Plinthes électriques0.0630.0050.0200.0850.0000.1310.1280.1370.1620.0000.1130.0000.0870.0000.1070.0000.0000.0000.0001.0000.0570.4880.1930.0770.0430.0000.0000.0000.0000.0690.0750.0000.0180.0280.0000.0000.0000.0000.0310.0000.0000.0000.0000.0000.000
Convecteurs0.0000.0870.0130.0500.0000.0700.0000.0000.0970.0000.0000.0170.0000.0000.0420.0170.0000.0390.0290.0571.0000.0000.0000.0000.1050.0000.0000.0000.0000.0000.0000.0000.0000.0000.0500.0000.0000.0000.0000.0000.0000.0000.1000.0120.017
Eau chaude0.1100.0550.0000.1240.0000.1630.1060.0980.2020.0220.1370.0000.0720.0000.0800.0000.0000.0110.0000.4880.0001.0000.1190.0000.0230.0000.0000.0000.0000.0100.0160.0550.0220.0570.0000.0000.0000.0200.0000.0000.0000.0250.0000.0080.000
Air soufflé (pulsé)0.0620.0770.0000.0460.0090.1110.0770.0960.1220.0420.0770.0000.0900.0750.0770.0000.0000.0230.0380.1930.0000.1191.0000.0000.1780.0000.0000.0000.0000.0470.0560.0000.0000.1050.0640.0000.0000.0000.0630.0000.0000.0070.0320.0000.000
Radiant0.0530.0000.0260.0000.0000.0400.0000.0400.0530.0000.0510.0290.0000.0000.0000.0290.0000.0000.0000.0770.0000.0000.0001.0000.0160.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0450.0000.0000.0000.0050.0000.000
Thermopompe0.0000.0290.0610.0000.0000.1360.0000.1120.1250.0510.0840.0330.0810.0670.1450.0330.0000.0000.0750.0430.1050.0230.1780.0161.0000.0000.0000.0000.0000.1140.1150.0170.0000.0540.1550.0000.0000.0160.0000.0000.0000.0980.1600.0000.050
Gaz naturel0.0000.0000.0000.0000.0230.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0490.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
Poêle à bois0.0000.0000.0500.0000.1130.0000.0000.0000.0370.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0270.0290.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
Foyer au gaz0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
water_access0.0000.0390.0000.1030.0000.0000.0000.0000.0250.0000.3010.0000.0760.0000.0000.0000.0170.2760.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
has_fireplace0.1560.0880.0000.0000.0720.1220.0580.1470.1930.0740.1670.0000.1500.0170.0400.0000.0000.0000.0620.0690.0000.0100.0470.0000.1140.0000.0270.0000.0001.0000.9580.0000.0000.0840.1080.0000.0000.0840.0000.0000.0000.0000.1130.0000.032
fireplace_func0.1650.0630.0000.0000.0610.1110.0620.1510.1910.0760.1650.0000.1510.0000.0500.0000.0000.0000.0690.0750.0000.0160.0560.0000.1150.0000.0290.0000.0000.9581.0000.0000.0000.0790.1080.0000.0000.0880.0000.0000.0000.0000.0920.0000.023
Porte de garage électrique0.0710.0390.0000.0000.0000.0580.0490.0650.0750.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0550.0000.0000.0170.0000.0000.0000.0000.0000.0001.0000.0000.0560.0600.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
Buanderie0.0000.0080.0000.0000.0000.0700.0000.0530.0000.0000.0000.0000.0000.0000.0000.0000.0110.0000.0000.0180.0000.0220.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0900.0000.0000.0000.0000.0000.0000.0790.0000.0000.0000.000
Climatiseur0.0800.0860.0000.0570.0880.1280.0750.1560.1580.0140.0860.0220.1280.0660.1170.0220.0000.0000.0660.0280.0000.0570.1050.0000.0540.0000.0000.0000.0000.0840.0790.0560.0901.0000.1890.0000.0000.0160.0000.0000.0000.0720.1750.0250.069
Aspirateur centrale0.0370.0370.0240.0230.0350.1310.0000.0840.0390.0000.0370.0000.0580.0490.0920.0000.0000.0000.1400.0000.0500.0000.0640.0000.1550.0000.0000.0000.0000.1080.1080.0600.0000.1891.0000.0160.0000.0000.0000.0400.0160.0740.2900.0370.054
Spa0.0000.0110.0000.0000.0760.0000.0000.0000.0000.0000.0000.0470.0000.0000.0000.0470.0000.0000.0260.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0161.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
Adapté pour personne à mobilité réduite0.0000.0000.0640.0000.0200.0000.0000.0000.0000.0000.0000.0280.0000.0000.0210.0280.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.000
Interphone0.0000.0000.0000.0000.0000.1260.0000.1510.0330.0000.2460.0000.0700.0000.1230.0000.0000.0000.0000.0000.0000.0200.0000.0000.0160.0000.0000.0000.0000.0840.0880.0000.0000.0160.0000.0000.0001.0000.0000.0000.0000.0000.0350.0000.015
Fournaise0.0000.0350.0000.0620.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0310.0000.0000.0630.0450.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.000
Planchers chauffant0.0080.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0400.0000.0000.0000.0001.0000.0000.0000.0000.0000.000
Ascenseur0.0000.0990.0000.0000.0620.0000.0000.0000.2120.0320.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0790.0000.0160.0000.0000.0000.0000.0001.0000.0000.0200.0000.000
Échangeur d'air0.0090.0000.0000.0340.0690.0000.0000.0000.2990.0000.0000.0000.0000.0390.0000.0000.0000.0000.0190.0000.0000.0250.0070.0000.0980.0000.0000.0000.0000.0000.0000.0000.0000.0720.0740.0000.0000.0000.0000.0000.0001.0000.0000.0000.044
Système d'alarme0.0750.0510.0480.0000.0000.0910.0000.0460.0790.0000.0000.0170.0970.0670.0450.0170.0000.0000.0290.0000.1000.0000.0320.0050.1600.0000.0000.0000.0000.1130.0920.0000.0000.1750.2900.0000.0000.0350.0000.0000.0200.0001.0000.1280.070
Borne de recharge0.0890.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0870.0000.0000.0000.0000.0490.0000.0120.0080.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0250.0370.0000.0000.0000.0000.0000.0000.0000.1281.0000.080
has_reno0.0330.0000.0000.0000.0000.0740.0000.1160.0000.0000.0610.0320.0530.9990.0940.0320.0000.0000.0470.0000.0170.0000.0000.0000.0500.0000.0000.0000.0000.0320.0230.0000.0000.0690.0540.0000.0000.0150.0000.0000.0000.0440.0700.0801.000

Missing values

2023-10-05T20:07:57.807304image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-10-05T20:07:58.008883image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-10-05T20:07:58.115522image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

IDpriceunitsincomeland_evalbuild_evalroomsbedroomswashroomsyear_builtliving_areayard_areahas_certificateyear_certificatedue_certificatenear_waterhas_pooltotal_parkingPlinthes électriquesConvecteursEau chaudeAir soufflé (pulsé)RadiantThermopompeGaz naturelPoêle à boisFoyer au gazwater_accesshas_fireplacefireplace_funcPorte de garage électriqueBuanderieClimatiseurAspirateur centraleSpaDétecteur d'incendie(relié)Détecteur d'incendie(non relié)Adapté pour personne à mobilité réduiteInterphoneFournaisePlanchers chauffantAscenseurÉchangeur d'airSystème d'alarmeBorne de rechargehas_renolast_year_reno
0110000003462002544007557008.03.0119002972.992500.4600000110000000000001000000000100000
127250003372002544005391005.03.0119102895.002500.4612022000110000000000000000000000000000
237250003375002544005136005.03.0119102897.642500.4612022000110000000000000000000000000000
349420003546602544009394005.03.0119101455.002500.4612005100210000100000000000000000000000
457320002324001494002741005.03.0119302292.002250.0000000210000000000000000000000000000
567750004397681494001747003.01.011915870.002249.6612018000210000000000000000000000000000
678380003462242279004729008.04.0219232039.112228.3400000110000000000000100000000000012019
788600002517802511003920008.04.0219531136.001680.0012021000201000100000000100000000000000
898950003513604088004515008.03.0119522573.653999.8712022001310000100000010010000000000000
9108750003405001328003627008.03.0119311175.001999.9312022000110100100000000000000000000000
IDpriceunitsincomeland_evalbuild_evalroomsbedroomswashroomsyear_builtliving_areayard_areahas_certificateyear_certificatedue_certificatenear_waterhas_pooltotal_parkingPlinthes électriquesConvecteursEau chaudeAir soufflé (pulsé)RadiantThermopompeGaz naturelPoêle à boisFoyer au gazwater_accesshas_fireplacefireplace_funcPorte de garage électriqueBuanderieClimatiseurAspirateur centraleSpaDétecteur d'incendie(relié)Détecteur d'incendie(non relié)Adapté pour personne à mobilité réduiteInterphoneFournaisePlanchers chauffantAscenseurÉchangeur d'airSystème d'alarmeBorne de rechargehas_renolast_year_reno
179717989500002216002938003602007.03.0219291127.502875.0412021000210000000000000000000000000000
179817999400004582003867006754006.03.0119261075.002775.0012022000310100000000000000000000000000
179918009750003522002323005836006.04.0219144433.652500.4612016000200100000000000000000000000000
180018017450003386401986003218006.03.0119261039.362249.6612022000110000000000001000000000000000
1801180287000034458030310056060010.03.0119411102.752249.6612023000100100000000000000000000000000
180218037450002386402704004732004.02.011910997.501819.1012016000100100000000000000000000000000
180318049210003438004274005495006.02.0119251100.002874.0012022000110000000000000000000000000000
180418059950002138242108003394004.02.011924777.372101.0012009100110000100000000000000000000000
180518069550005553204088007028005.02.0119261005.002750.0011984100110100000000000000000000000000
180618079890002492001839004319007.02.0119201075.001800.0011997100111000000000000000000000000012013